Energy storage battery consistency evaluation method and device and computer equipment

Abstract

The invention relates to the technical field of battery state evaluation, and discloses an energy storage battery consistency evaluation method and device and computer equipment, and the method comprises the steps: obtaining the voltage, current and capacity of a to-be-evaluated energy storage battery; based on the voltage, the current and the capacity, extracting consistency evaluation characteristics of the energy storage battery to be evaluated; quantifying the consistency evaluation score of each single feature in the consistency evaluation features; allocating a weight to each single feature in the consistency assessment features, and determining a consistency assessment comprehensive score of the energy storage battery to be assessed based on the consistency assessment score of each single feature and each single feature weight; and determining the health level of the to-be-evaluated energy storage battery based on the consistency evaluation comprehensive score of the to-be-evaluated energy storage battery. According to the invention, a comprehensive and objective lithium ion battery consistency evaluation result can be provided, and operation and maintenance personnel are helped to optimize battery management, so that the safety and stability of an energy storage system are improved, and the service life of the energy storage system is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of battery status assessment, and in particular to a method, device and computer equipment for evaluating the consistency of energy storage batteries. Background Art

[0002] Lithium-ion batteries, due to their advantages such as high energy density, high power density, environmental friendliness, and low self-discharge, have been applied to numerous energy storage systems, including electric vehicles, grid-level energy storage, and consumer electronics. In practical applications, hundreds or even thousands of single-cell batteries are connected in series, parallel, or in a combination of series and parallel to meet the high voltage and high power requirements of full load. As lithium-ion batteries operate, differences in capacity, internal resistance, open-circuit voltage, and state of charge develop between cells. This differentiation can lead to a "barrel effect" within the lithium-ion battery module, reducing the consistency of the battery pack and threatening the safety of the system. Therefore, conducting consistency evaluations of lithium-ion batteries is of great significance for their operation and maintenance.

[0003] Existing consistency assessment methods include: methods based on signal time-frequency domain analysis, that is, extracting statistical features such as mean value, standard deviation and other statistical features from the time-frequency domain of easily measurable signals. This method is simple to calculate, but the extracted features are easily interfered by noise; in addition, publication number CN115021344 discloses a method for assessing the inconsistency of substation battery packs. The features include the internal resistance value obtained by timed testing, which requires online static processing and is difficult to meet real-time requirements, thereby limiting the online real-time application of consistency assessment.

[0004] The battery consistency assessment method in the existing technology that uses a single feature and is difficult to meet real-time requirements has limitations and cannot fully reflect the status of the energy storage battery, reducing the accuracy of the assessment. Summary of the Invention

[0005] In view of this, the present invention provides a method, apparatus and computer equipment for evaluating the consistency of energy storage batteries to solve the limitations of the single feature evaluation method in the prior art and the problem of difficulty in meeting real-time requirements.

[0006] In a first aspect, the present invention provides a method for evaluating the consistency of an energy storage battery, the method comprising:

[0007] Obtain the voltage, current and capacity of the energy storage battery to be evaluated;

[0008] Extract the consistency assessment features of the energy storage battery to be evaluated based on voltage, current and capacity;

[0009] Quantify the consistency assessment score of each single feature in the consistency assessment features;

[0010] Assign a weight to each single feature in the consistency assessment feature, and determine the comprehensive consistency assessment score of the energy storage battery to be assessed based on the consistency assessment score of each single feature and the weight of each single feature;

[0011] The health level of the energy storage battery to be evaluated is determined based on the comprehensive score of the consistency assessment of the energy storage battery to be evaluated.

[0012] The energy storage battery consistency evaluation method provided by the present invention directly reads voltage, current and capacity data from the battery management system, avoids manual collection errors, ensures that the data can reflect the battery operating status in real time, and lays a real data foundation for subsequent evaluation. The capacity, ohmic internal resistance, and open circuit voltage are extracted as evaluation features. These parameters are directly related to the battery charge and discharge performance and internal state, and can accurately characterize the battery consistency differences and avoid irrelevant features interfering with the evaluation accuracy. The single feature score is quantified, and the subjective judgment bias is eliminated by calculating the correlation between data, so that the single performance indicators of different batteries are comparable, providing a quantitative basis for comprehensive evaluation. The single feature score is weighted and summed with the combined weight to form a comprehensive evaluation score, which comprehensively integrates multi-dimensional feature information, avoids single indicator misjudgment, and can systematically reflect the overall consistency level of the battery. Based on the comprehensive score of the consistency evaluation of the energy storage battery to be evaluated, the health level of the energy storage battery to be evaluated is determined, and the quantitative score is converted into an intuitive health status classification, which facilitates operation and maintenance personnel to quickly locate battery problems and formulate maintenance strategies. It can provide comprehensive and objective lithium-ion battery consistency evaluation results, help operation and maintenance personnel optimize battery management, and thus improve the safety, stability and service life of the energy storage system.

[0013] In an optional embodiment, based on voltage, current, and capacity, the consistency assessment features of the energy storage battery to be assessed are extracted, including:

[0014] The ohmic internal resistance and open circuit voltage of the energy storage battery are extracted based on voltage and current, and the ohmic internal resistance, open circuit voltage and capacity are used as consistency evaluation features of the energy storage battery to be evaluated.

[0015] The energy storage battery consistency evaluation method provided by the present invention extracts ohmic internal resistance, open circuit voltage, and capacity to describe battery performance from different dimensions. Ohmic internal resistance characterizes the power characteristics of the battery. Differences in internal resistance will affect the voltage drop during battery charging and discharging, resulting in inconsistent output power of each battery in the battery pack; open circuit voltage is related to the chemical equilibrium state of the battery and can intuitively reflect the energy difference between batteries; and capacity directly reflects the energy storage capacity of the battery. The combination of the three forms a three-dimensional portrayal of battery performance, comprehensively covering important aspects such as battery power, energy, and energy storage capacity. Compared with a single feature or a combination of partial features, it can more completely evaluate the consistency of the battery and reduce evaluation blind spots.

[0016] In an optional embodiment, extracting the ohmic internal resistance and open circuit voltage of the energy storage battery based on the voltage and current includes:

[0017] Extract the ohmic internal resistance and open circuit voltage of the energy storage battery based on voltage and current, including:

[0018] Establish the energy storage battery kinetic equation based on voltage and current;

[0019] Discretize the energy storage battery dynamics equation, and establish a recursive least squares equation with forgetting factor based on the discretized equation;

[0020] Establishing a parameter matrix equation and a data matrix based on a recursive least squares method equation with a forgetting factor, and establishing a recursive least squares method recursive equation with a forgetting factor based on the parameter matrix and the data matrix;

[0021] The system equation of the energy storage battery is established based on the recursive least squares method with a forgetting factor, and the system equation is instantiated to identify the ohmic internal resistance and open circuit voltage of the energy storage battery.

[0022] The energy storage battery consistency assessment method provided by the present invention uses a first-order equivalent circuit model, taking into account the internal physical properties of the battery. It also transforms the model into a solvable mathematical problem through discretization and parameter identification, thus balancing model complexity and computational efficiency. The introduction of a forgetting factor enables the algorithm to have an exponentially decaying memory of historical data, giving priority to the latest data and effectively tracking the time-varying characteristics of battery parameters as they change with aging and operating conditions. During battery aging, the ohmic internal resistance gradually increases. The forgetting factor ensures that the algorithm can promptly capture this change, preventing historical data from interfering with current parameter estimates. Based on the discretized equations, a recursive least squares method with a forgetting factor is established to implement online parameter updates. This eliminates the need to store all historical data, reduces computational complexity, and is suitable for real-time operation in embedded systems. Each sampling only requires matrix operations, resulting in low computational complexity and meeting the real-time requirements of the battery management system. The ohmic internal resistance and open-circuit voltage identified using the forgetting factor recursive least squares method accurately reflect the actual battery state, providing a reliable basis for consistency assessment.

[0023] In an optional embodiment, instantiating the system equation to identify the ohmic internal resistance and open circuit voltage of the energy storage battery includes:

[0024] The parameter matrix of the energy storage battery to be evaluated at all times is calculated based on the voltage and current, and the parameter matrix is ​​substituted into the system equation to identify the ohmic internal resistance and open-circuit voltage of the energy storage battery.

[0025] The energy storage battery consistency assessment method provided by this invention calculates a parameter matrix at all times based on voltage and current. This method fully utilizes dynamic data during battery operation and accurately captures the complex physical and chemical changes within the battery in the form of data. Substituting the parameter matrix into the system equation for identification, and using optimization algorithms such as the least squares method, the calculated ohmic internal resistance and open-circuit voltage are highly consistent with the actual battery parameters, effectively reducing identification errors.

[0026] In an optional embodiment, the consistency assessment score of each single feature in the consistency assessment features is quantified, including:

[0027] The grey correlation analysis method is used to quantify the consistency evaluation score of each single feature in the consistency evaluation feature. The formula of the grey correlation analysis method is expressed as follows:

[0028]

[0029] Among them, ξ i (k') is the consistency evaluation score for each single feature, is the absolute value difference, represents the reference sequence, represents the sample sequence to be evaluated, and ρ is the resolution coefficient.

[0030] The energy storage battery consistency assessment method provided by this invention uses gray correlation analysis, which only requires a small number of samples (typically ≥4 groups) to establish an effective analysis model. By analyzing the geometric similarity of limited data sequences such as voltage, current, and capacity, it explores potential correlations between data. Compared to statistical methods (such as regression analysis) that require a large number of samples, gray correlation analysis can still maintain high assessment accuracy in small sample scenarios.

[0031] In an optional embodiment, a weight is assigned to each single feature in the consistency evaluation feature, and a comprehensive consistency evaluation score of the energy storage battery to be evaluated is determined based on the consistency evaluation score of each single feature and the weight of each single feature, including:

[0032] The order relationship analysis method and CRITIC objective weight allocation method are used to assign weights to ohmic internal resistance, open circuit voltage and capacity respectively, and the ohmic internal resistance weight coefficient, open circuit voltage weight coefficient and capacity weight coefficient are obtained;

[0033] The first product of the ohmic internal resistance consistency assessment score and the ohmic internal resistance weight coefficient, the second product of the open circuit voltage consistency assessment score and the open circuit voltage weight coefficient, and the third product of the capacity consistency assessment score and the capacity weight coefficient are calculated respectively, and the first product, the second product and the third product are added together to obtain the comprehensive consistency assessment score of the energy storage battery to be evaluated.

[0034] In an optional embodiment, the order relationship analysis method and the CRITIC objective weight allocation method are used to allocate weights to the ohmic internal resistance, open circuit voltage and capacity respectively, and the ohmic internal resistance weight coefficient, the open circuit voltage weight coefficient and the capacity weight coefficient are obtained, including:

[0035] The order relationship analysis method is used to rank the importance of ohmic internal resistance, open circuit voltage and capacity, and the relative weight ratios between ohmic internal resistance, open circuit voltage and capacity are calculated based on the order of importance. Based on the relative weight ratios between the two, the subjective weight coefficients of ohmic internal resistance, open circuit voltage and capacity are obtained;

[0036] The ohmic internal resistance, open circuit voltage and capacity are all used as evaluation indicators. An indicator matrix is ​​constructed based on the evaluation indicators. Each indicator in the indicator matrix is ​​normalized, and the indicator variability and indicator conflict of each indicator after normalization are calculated respectively.

[0037] The information content of each indicator is determined based on the indicator heterogeneity and indicator conflict of each indicator, and the objective weight coefficient of each indicator is determined based on the information content of each indicator using the CRITIC objective weight allocation method. The objective weight coefficient of each indicator includes the objective weight coefficient of ohmic internal resistance, the objective weight coefficient of open circuit voltage and the objective weight coefficient of capacity;

[0038] The subjective weight coefficient of the ohmic internal resistance and the objective weight coefficient of the ohmic internal resistance, the subjective weight coefficient of the open-circuit voltage and the objective weight coefficient of the open-circuit voltage, and the subjective weight coefficient of the capacity and the objective weight coefficient of the capacity are respectively combined using the ensemble average method to obtain the ohmic internal resistance weight coefficient, the open-circuit voltage weight coefficient and the capacity weight coefficient.

[0039] The energy storage battery consistency assessment method provided by the present invention innovatively uses a subjective and objective combination method to assign weights, combines the order relationship analysis method to determine the subjective weight, and the CRITIC objective weight allocation method to determine the objective weight. The combined weight is obtained through geometric mean calculation, and the subjective and objective weights are integrated through geometric mean. This not only retains the expert's understanding of the essence of the characteristics, but also dynamically adjusts according to the characteristics of the data, making the assessment more objective and comprehensive.

[0040] In an optional embodiment, determining the health level of the energy storage battery to be evaluated based on the comprehensive consistency evaluation score of the energy storage battery to be evaluated includes:

[0041] Determine the mapping relationship between the comprehensive consistency assessment score of the energy storage battery to be evaluated and the preset health level standard range, and determine the health level of the energy storage battery to be evaluated based on the mapping relationship.

[0042] The energy storage battery consistency assessment method provided by this invention provides a clear and unified basis for determining the health status of energy storage batteries by presetting standard health grade intervals. Compared to vague qualitative descriptions, this quantitative mapping relationship avoids assessment bias caused by subjective judgment. It converts complex consistency assessment scores into simple health grade classifications, presenting battery health status in an intuitive manner.

[0043] In a second aspect, the present invention provides a device for evaluating the consistency of an energy storage battery, the device comprising:

[0044] A data acquisition module is used to obtain the voltage, current and capacity of the energy storage battery to be evaluated;

[0045] A feature extraction module is used to extract consistency assessment features of the energy storage battery to be evaluated based on voltage, current, and capacity;

[0046] A consistency quantification module is used to quantify the consistency evaluation score of each single feature in the consistency evaluation features;

[0047] A comprehensive score determination module is used to assign a weight to each single feature in the consistency assessment feature, and determine the comprehensive consistency assessment score of the energy storage battery to be assessed based on the consistency assessment score of each single feature and the weight of each single feature;

[0048] The evaluation module is used to determine the health level of the energy storage battery to be evaluated based on the comprehensive score of the consistency evaluation of the energy storage battery to be evaluated.

[0049] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the energy storage battery consistency assessment method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0050] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the energy storage battery consistency assessment method of the first aspect or any corresponding embodiment thereof.

[0051] In a fifth aspect, the present invention provides a computer program product comprising computer instructions, the computer instructions being used to enable a computer to execute the energy storage battery consistency assessment method of the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0053] Figure 1 is a flow chart of a method for evaluating consistency of an energy storage battery according to an embodiment of the present invention;

[0054] Figure 2 is a flow chart of another method for evaluating consistency of an energy storage battery according to an embodiment of the present invention;

[0055] Figure 3 is a flow chart of another method for evaluating consistency of energy storage batteries according to an embodiment of the present invention;

[0056] Figure 4 is a flow chart of another method for evaluating consistency of an energy storage battery according to an embodiment of the present invention;

[0057] Figure 5 is a schematic diagram of a first-order equivalent circuit of an energy storage battery according to an embodiment of the present invention;

[0058] Figure 6 2 is a schematic diagram of an application of a method for evaluating consistency of an energy storage battery according to an embodiment of the present invention;

[0059] Figure 7 is a structural block diagram of a device for evaluating consistency of an energy storage battery according to an embodiment of the present invention;

[0060] Figure 8 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0061] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0062] The energy storage battery consistency assessment methods in related technologies involve: ① Model-based methods, including parameter estimation and state estimation, that is, by establishing an equivalent model of the energy storage lithium-ion battery, on the one hand, using parameter identification methods to identify the characteristic parameters of the lithium-ion battery, such as the ohmic internal resistance, polarization internal resistance, and open-circuit voltage, and extracting the differentiated features of different battery identification parameters. On the other hand, using state estimation methods such as Kalman filtering and unscented Kalman filtering to estimate the state of charge, health state, power state, etc. of the lithium-ion battery, further consistency assessment features can be extracted from the estimated state. This method relies on the calculation accuracy of the model, and a single parameter is difficult to fully characterize the overall performance of the battery. ② Based on information fusion technology, that is, extracting multiple evaluation features from the battery's electrothermal characteristic parameters, and using fusion methods to integrate these evaluation features to achieve consistency evaluation of the energy storage battery. This method can fully characterize the advantages of battery consistency, but the fusion strategy of multiple evaluation features needs further research.

[0063] An embodiment of the present invention provides a method for evaluating the consistency of energy storage batteries. By extracting multiple consistency evaluation features, assigning weights to each evaluation feature, and calculating a single feature evaluation score, a comprehensive score of the multi-feature fusion of the energy storage battery consistency is finally obtained, and the overall health level of the battery to be evaluated is further determined, thereby achieving the effect of providing comprehensive and objective consistency evaluation results for lithium-ion batteries.

[0064] According to an embodiment of the present invention, an embodiment of a method for evaluating consistency of an energy storage battery is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0065] In this embodiment, a method for evaluating the consistency of energy storage batteries is provided, which can be used in energy storage management systems. Figure 1 FIG. 1 is a flow chart of a method for evaluating consistency of an energy storage battery according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0066] Step S101: Obtain the voltage, current, and capacity of the energy storage battery to be evaluated.

[0067] Specifically, the energy storage battery in the embodiment of the present invention takes an energy storage lithium-ion battery as an example, and uses the energy storage battery management system to collect the voltage, current and capacity data of each battery cell of the energy storage lithium-ion battery to be evaluated, that is, the voltage, current and capacity data are directly read from the energy storage lithium-ion battery management system.

[0068] Step S102: extracting consistency evaluation features of the energy storage battery to be evaluated based on voltage, current, and capacity.

[0069] Specifically, the ohmic internal resistance and open circuit voltage of the energy storage lithium ion battery are identified based on the voltage and current data of the energy storage lithium ion battery obtained in step S101, and the ohmic internal resistance, open circuit voltage and capacity are used as consistency evaluation features of the energy storage lithium ion battery to be evaluated.

[0070] Step S103: quantify the consistency evaluation score of each single feature in the consistency evaluation feature.

[0071] Specifically, each single feature refers to ohmic internal resistance, open circuit voltage, and capacity. Grey correlation analysis is used to quantify the consistency evaluation score of each single feature of ohmic internal resistance, open circuit voltage, and capacity of energy storage lithium-ion batteries.

[0072] Step S104 : assigning a weight to each single feature in the consistency evaluation features, and determining a comprehensive consistency evaluation score of the energy storage battery to be evaluated based on the consistency evaluation score of each single feature and the weight of each single feature.

[0073] Specifically, the weight coefficients of the three characteristics of the energy storage lithium-ion battery consistency assessment are assigned based on the order relationship analysis method and the CRITIC combination method. The comprehensive score of the energy storage lithium-ion battery consistency assessment is determined based on the weights of the three characteristics and the consistency assessment score of the single characteristic of the energy storage lithium-ion battery in step S103.

[0074] Step S105 : determining the health level of the energy storage battery to be evaluated based on the comprehensive score of the consistency evaluation of the energy storage battery to be evaluated.

[0075] Specifically, based on the mapping between the consistency evaluation score and the health grade of the energy storage lithium-ion battery, the overall health grade of the energy storage lithium-ion battery to be evaluated is determined according to the consistency evaluation score of the energy storage lithium-ion battery to be evaluated.

[0076] The energy storage battery consistency assessment method provided in this embodiment directly reads voltage, current, and capacity data from the battery management system, avoiding manual data collection errors and ensuring that the data reflects the battery's operating status in real time, laying a solid foundation for subsequent evaluation. It extracts capacity, ohmic internal resistance, and open-circuit voltage as evaluation features. These parameters are directly related to the battery's charge and discharge performance and internal state, accurately characterizing differences in battery consistency and preventing irrelevant features from interfering with evaluation accuracy. It quantifies single feature scores and eliminates subjective judgment bias through data correlation calculations, making single performance indicators of different batteries comparable and providing a quantitative basis for comprehensive evaluation. The single feature score is weighted and summed with the combined weights to form a comprehensive assessment score. This comprehensively integrates multi-dimensional feature information, avoids misjudgment of a single indicator, and systematically reflects the overall consistency level of the battery. Based on the comprehensive consistency assessment score of the energy storage battery to be evaluated, the health level of the energy storage battery to be evaluated is determined. The quantitative score is converted into an intuitive health status classification, allowing operators and maintenance personnel to quickly identify battery problems and formulate maintenance strategies. This method provides comprehensive and objective lithium-ion battery consistency assessment results, helping operators and maintenance personnel optimize battery management, thereby improving the safety, stability, and service life of the energy storage system.

[0077] In this embodiment, a method for evaluating the consistency of an energy storage battery is provided, which can be used in an energy storage battery management system. Figure 2 FIG. 1 is a flow chart of a method for evaluating consistency of an energy storage battery according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0078] Step S201: Obtain the voltage, current, and capacity of the energy storage battery to be evaluated. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0079] Step S202 : extracting consistency evaluation features of the energy storage battery to be evaluated based on voltage, current, and capacity.

[0080] Specifically, in the embodiments of the present invention, features are selected based on the principle of describing the battery's characteristics as comprehensively as possible and being easily accessible. The present invention selects the ohmic internal resistance, open-circuit voltage, and capacity of energy storage lithium-ion batteries as consistency assessment features for energy storage lithium-ion batteries. Capacity information is statistically analyzed based on data recorded by the BMS (Battery Management System, or BMS), and the capacity data is presented in the form of a time series. The ohmic internal resistance and open-circuit voltage of the energy storage lithium-ion battery are identified by establishing an equivalent first-order circuit model of the energy storage battery. Using the kinetic equations of the equivalent circuit and the least squares parameter identification method, the ohmic internal resistance and open-circuit voltage of the energy storage lithium-ion battery are used as input and the voltage of the energy storage battery as the observed quantity.

[0081] The above step S202 includes:

[0082] Step a: extracting the ohmic internal resistance and open circuit voltage of the energy storage battery based on voltage and current, and using the ohmic internal resistance, open circuit voltage and capacity as consistency evaluation features of the energy storage battery to be evaluated.

[0083] In some optional embodiments, the above step a includes:

[0084] Step a1: Establishing a kinetic equation of the energy storage battery based on voltage and current.

[0085] Specifically, based on the electrical characteristics of energy storage lithium-ion batteries, a first-order equivalent circuit model of energy storage lithium-ion batteries was established. There are also second-order and third-order equivalent models, but the calculation process is similar and will not be repeated here. The equivalent first-order circuit of energy storage lithium-ion batteries is as follows: Figure 5 As shown, where V p Indicates polarization voltage, V ocv represents the open circuit voltage, R0 represents the ohmic internal resistance, C p Represents polarized capacitance, R p Represents polarization resistance, V t Represents the terminal voltage, I represents the current flowing through the battery, and the battery terminal voltage and current can be measured and stored by the energy storage lithium-ion battery management system.

[0086] Based on the knowledge of circuit principles, the kinetic equation of the first-order equivalent circuit of the energy storage lithium-ion battery can be established as shown below:

[0087]

[0088] Where V ocv , R0, C p and R p The parameters are all unknown, and therefore an offline or online identification method is required to identify the unknown parameters. In the embodiment of the present invention, a recursive least squares parameter identification method with a forgetting factor is adopted.

[0089] Step a2: discretize the energy storage battery kinetic equation, and establish a recursive least squares equation with a forgetting factor based on the discretized equation.

[0090] Specifically, if the difference equation of the unidentified system model is:

[0091]

[0092] In the formula, y(k) represents the system output, u(k) represents the system input, e(k) represents the noise, k represents the time point, i is the index, which takes values ​​1, 2, 3…, n, and is used to traverse the number of steps of historical data, a1, a2,…, a nis the autoregressive coefficient of the system output, which is used to describe the dependence of the system output on historical values, b1, b2, ..., b n is the input response coefficient of the system, which describes the response of the system output to the past input.

[0093] If:

[0094]

[0095] Where θ is the system parameter vector to be identified, is the parameter matrix, and φ(k) is the data matrix.

[0096] The above formula can be written in the conventional form of least squares method:

[0097] y=φθ T +e(4);

[0098] Among them, θ T Indicates the transpose operation of the parameter matrix, and y is the result of the least squares method.

[0099] If the input and output are expanded to N dimensions, the form of the least squares method remains unchanged, and the corresponding data matrix dimension changes. The specific data matrix is ​​not given here.

[0100] Take the functional J(θ) as follows:

[0101]

[0102] The calculation principle of the least squares method is to update the parameters to be identified so that the functional takes the minimum value. Then when the functional takes the minimum value, the functional derivative is set to zero. At this time, the matrix of the parameters to be identified is expressed as:

[0103]

[0104] In order to identify the parameter matrix of the battery cell to be identified using the least squares method, the kinetic equation of the energy storage lithium-ion battery needs to be rewritten into the form of the least squares method. The discretization method is used to discretize formula (1). The differential equation of the energy storage lithium-ion battery system after processing is:

[0105]

[0106] Where Δt is the sampling period, which is 1s here.

[0107] To make the expression more intuitive and simpler, we introduce intermediate variables and rewrite the above equation as follows:

[0108] V t (k)=(1-c1)V ocv (k)+c1V t(k-1)+c2I(k)+c3I(k-1) (8);

[0109] Where V t (k) is the system output, I(k) is the system input, c1, c2, c3 are the coefficients of the discretized equations, and the coefficients of each differential equation can be determined according to (7) as follows:

[0110]

[0111] Then corresponding to the system least squares form, we can let V t (k) = y(k), that is, the terminal voltage of the battery is the output variable of the energy storage lithium-ion battery system. Considering the existence of Gaussian white noise e(k) with a mean of 0 in actual conditions, the energy storage lithium-ion battery system can be expressed as follows:

[0112] y(k)=φ(k)θ(k)+e(k) (10).

[0113] Step a3: establishing a parameter matrix equation and a data matrix based on a recursive least squares method equation with a forgetting factor, and establishing a recursive least squares method recursive equation with a forgetting factor based on the parameter matrix and the data matrix.

[0114] Specifically, according to formula (10), the parameter matrix and data matrix in the least square form of the energy storage lithium-ion battery system can be determined as follows:

[0115]

[0116] Where θ(k) and φ(k) are the parameter matrix and data matrix, respectively. I(k) and I(k-1) are the currents flowing through the battery at the kth moment and the k-1th moment, respectively.

[0117] Establish the recursive least squares method with forgetting factor recursive equation:

[0118]

[0119] in, is the estimated value of the parameter to be identified at time k-1, is the estimated value of the parameter to be identified at time k, K(k) is the gain coefficient matrix of the algorithm at time k, y(k) is the system output at time k, φ T (k) is the transposed matrix of the system parameter matrix at time k, P(k-1) is the error covariance matrix of the predicted value of the state variable at time k-1, and λ is the forgetting factor.

[0120] Step a4: establishing a system equation of the energy storage battery based on a recursive least squares method with a forgetting factor, instantiating the system equation, and identifying the ohmic internal resistance and open circuit voltage of the energy storage battery.

[0121] Specifically, the system equation is established:

[0122]

[0123] Where Δt is the sampling period, and θ0, θ1, θ2, and θ3 are the parameter matrices identified using the recursive least squares method with a forgetting factor.

[0124] In the above step a4, the system equation is instantiated to identify the ohmic internal resistance and open circuit voltage of the energy storage battery, including:

[0125] The parameter matrix of the energy storage battery to be evaluated at all times is calculated based on the voltage and current, and the parameter matrix is ​​substituted into the system equation to identify the ohmic internal resistance and open-circuit voltage of the energy storage battery.

[0126] Furthermore, a parameter identification process based on the recursive least squares method with a forgetting factor is instantiated: the system parameters θ(0), P(0), and the forgetting factor λ are initialized. The parameter matrix θ of the battery to be evaluated at all times is calculated based on the voltage and current data collected in step S201 and formula (12). Substituting the parameter matrix into formula (13) can obtain the coefficients on the left side of formula (13), i.e., the open circuit voltage and ohmic internal resistance in the equivalent circuit to be identified. The above-mentioned initialized system parameters θ(0) are arbitrary values, P(0) = ΨI', and should be as large as possible, where I' is the unit matrix, the coefficient Ψ is 8000, and the initialized forgetting factor λ is 0.98.

[0127] The energy storage battery consistency assessment method provided in this embodiment utilizes a first-order equivalent circuit model, taking into account the battery's internal physical properties. Through discretization and parameter identification, the model is transformed into a solvable mathematical problem, balancing model complexity and computational efficiency. The introduction of a forgetting factor enables the algorithm to retain an exponentially decaying memory of historical data, prioritizing the most recent data and effectively tracking the time-varying characteristics of battery parameters as they age and change under varying operating conditions. As batteries age, the ohmic internal resistance gradually increases. The forgetting factor ensures that the algorithm can promptly capture this change, preventing historical data from interfering with current parameter estimates. Based on the discretized equations, a recursive least squares method with a forgetting factor is established to implement online parameter updates. This eliminates the need to store all historical data, reduces computational complexity, and is suitable for real-time operation in embedded systems. Each sampling step requires only matrix operations, resulting in low computational complexity and meeting the real-time requirements of battery management systems. The ohmic internal resistance and open-circuit voltage identified using the forgetting factor recursive least squares method accurately reflect the actual battery state, providing a reliable basis for consistency assessment.

[0128] Step S203: quantify the consistency evaluation score of each single feature in the consistency evaluation feature.

[0129] Specifically, the above step S203 includes:

[0130] Step b: using grey relational analysis to quantify the consistency evaluation score of each single feature in the consistency evaluation feature, wherein the formula of grey relational analysis is expressed as:

[0131]

[0132] Among them, ξ i (k') is the consistency evaluation score for each single feature, is the absolute value difference, represents the reference sequence, represents the sample sequence to be evaluated, ρ is the resolution coefficient, and its value range is generally 0.1-0.5. ρ = 0.5 is often used to balance the influence of extremely large and extremely small differences.

[0133] Specifically, in an embodiment of the present invention, the ohmic internal resistance, open circuit voltage and capacity of the energy storage lithium-ion battery are normalized, and the correlation between the consistency evaluation characteristics of each energy storage lithium-ion battery and the reference value is calculated using the grey correlation analysis method, and the correlation is used as the consistency evaluation score of each energy storage lithium-ion battery.

[0134] Specifically, the calculation procedure of the grey relational analysis method includes:

[0135] Determine the object to be evaluated and the index. Suppose there are h sequences to be evaluated and a reference sequence, which can be expressed as: reference sequence X0 = {x0(1), x0(2), ..., x0(o)}, comparison sequence X i ={x i (1),x i (2),...,x i (o)}, i = 1, 2, ..., h, where o represents the number of feature data collected in the current evaluation window. Since different evaluation indicators have different dimensions, the data needs to be dimensionless. The normalization calculation formula is shown as follows:

[0136]

[0137] Among them, x i (k') represents the k'th value in the reference sequence or comparison sequence, where k ranges from 1, 2, ..., h.

[0138] The normalized data matrix can be expressed as:

[0139]

[0140] The correlation coefficient is calculated to measure the closeness between the comparison sequence and the reference sequence at each evaluation point. The calculation formula is:

[0141]

[0142] in, is the absolute difference, i.e., the absolute value of the difference between the kth sample in the reference sequence and the kth sample in the comparison sequence after normalization. ρ is the resolution coefficient, which generally ranges from 0.1 to 0.5, with ρ = 0.5 often used to balance the effects of very large and very small differences.

[0143] The correlation coefficient matrix obtained by calculation of formula (13) is ξ=[ξ(1),ξ(2)…ξ(h)]. The grey correlation coefficient is obtained by averaging the correlation coefficient matrix. Its calculation formula is:

[0144]

[0145] Specifically, taking ohmic resistance as an example, the following calculation procedure is also applicable to the other two evaluation features. If a total of 5 feature points are obtained in the current evaluation time window, and there are 3 single cells in the energy storage lithium-ion battery module to be evaluated, and the data has been normalized, then the ohmic resistance feature can be expressed as:

[0146]

[0147] Each column of the above ohmic resistance characteristic matrix represents a characteristic point, and each row represents a different battery. Therefore, the data matrix has a shape of 3×5. In the embodiment of the present invention, the mean value of all batteries for the same characteristic point is used as the reference sequence. For the ohmic resistance characteristic matrix of three batteries, the reference sequence is the mean value of each column. Therefore, the shape of the ohmic resistance reference sequence is 1×5, which can be expressed as

[0148] According to formula (13), the grey correlation value ξ of the three single cells to the reference sequence can be calculated: 欧姆电阻 =[ξ 欧姆电阻 (1)ξ 欧姆电阻 (2)ξ 欧姆电阻 (3)], take the average value of the gray correlation value sequence of the ohmic resistance of the energy storage lithium-ion battery, and you can get the overall gray correlation value of the energy storage lithium-ion battery module composed of 3 single cells in the current time window, which also represents the consistency evaluation result. For details, please see Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0149] Step S204: assign a weight to each single feature in the consistency assessment feature, and determine the comprehensive consistency assessment score of the energy storage battery to be assessed based on the consistency assessment score of each single feature and the weight of each single feature. Figure 1 Step S104 of the illustrated embodiment will not be described in detail here.

[0150] Step S205: Determine the health level of the energy storage battery to be evaluated based on the comprehensive score of the consistency evaluation of the energy storage battery to be evaluated. Figure 1 Step S105 of the illustrated embodiment will not be described in detail here.

[0151] The energy storage battery consistency evaluation method provided in this embodiment describes the battery performance from different dimensions by extracting ohmic internal resistance, open circuit voltage and capacity. Ohmic internal resistance characterizes the power characteristics of the battery. The difference in internal resistance will affect the voltage drop during battery charging and discharging, resulting in inconsistent output power of each battery in the battery pack; open circuit voltage is related to the chemical equilibrium state of the battery and can intuitively reflect the energy difference between batteries; capacity directly reflects the energy storage capacity of the battery. The combination of the three forms a three-dimensional portrayal of battery performance, comprehensively covering important aspects such as battery power, energy and energy storage capacity. Compared with a single feature or a combination of partial features, it can more completely evaluate the consistency of the battery and reduce evaluation blind spots. By calculating the parameter matrix at all times based on voltage and current, the dynamic data during battery operation can be fully utilized, and the complex physical and chemical changes inside the battery can be accurately captured in the form of data. The parameter matrix is ​​substituted into the system equation for identification, and optimization algorithms such as the least squares method are used to make the calculated ohmic internal resistance and open circuit voltage highly consistent with the actual battery parameters, effectively reducing the identification error.

[0152] In this embodiment, a method for evaluating the consistency of energy storage batteries is provided, which can be used in energy storage management systems. Figure 3 FIG. 1 is a flow chart of a method for evaluating consistency of an energy storage battery according to an embodiment of the present invention. Figure 3 As shown, the process includes the following steps:

[0153] Step S301: Obtain the voltage, current, and capacity of the energy storage battery to be evaluated. Figure 2 Step S201 of the illustrated embodiment will not be described in detail here.

[0154] Step S302: Extract the consistency evaluation features of the energy storage battery to be evaluated based on voltage, current and capacity. Figure 2 Step S202 of the illustrated embodiment will not be described in detail here.

[0155] Step S303: quantify the consistency evaluation score of each single feature in the consistency evaluation feature. Figure 2Step S203 of the illustrated embodiment will not be described in detail here.

[0156] Step S304: assign a weight to each single feature in the consistency evaluation feature, and determine a comprehensive consistency evaluation score of the energy storage battery to be evaluated based on the consistency evaluation score of each single feature and the weight of each single feature.

[0157] Specifically, based on the evaluation scores of the single characteristics of the battery pack to be tested, namely, ohmic resistance, open circuit voltage, and capacity, obtained in step S303, each single evaluation characteristic is weighted to determine a mathematical relationship between the comprehensive consistency evaluation score of the energy storage lithium-ion battery to be evaluated and the consistency evaluation score of each single characteristic lithium-ion battery.

[0158] In the example of the present invention, considering that the degree of influence of each evaluation feature on the consistency of energy storage lithium-ion batteries is different, the weight of a single evaluation feature is allocated by a combined weighting method combining the ordinal relationship analysis method and the CRITIC objective weight allocation method, so as to determine the comprehensive consistency evaluation score of the energy storage lithium-ion battery based on the single feature evaluation result and the corresponding weight coefficient.

[0159] The above step S304 includes:

[0160] Step S3041 , using the order relationship analysis method and the CRITIC objective weight allocation method to allocate weights to the ohmic internal resistance, open circuit voltage, and capacity, respectively, to obtain the ohmic internal resistance weight coefficient, the open circuit voltage weight coefficient, and the capacity weight coefficient.

[0161] In some optional implementations, step S3041 includes:

[0162] Step c1: Use the order relationship analysis method to rank the importance of ohmic internal resistance, open circuit voltage and capacity, and calculate the relative weight ratio between ohmic internal resistance, open circuit voltage and capacity based on the order of importance ranking, and obtain the subjective weight coefficient of ohmic internal resistance, open circuit voltage and capacity based on the relative weight ratio between them.

[0163] Specifically, the ordinal relationship analysis method is a subjective weighting method based on the improved hierarchical analysis method. Its basic steps include: first, it is necessary to determine the ranking relationship of the consistency evaluation characteristics of the energy storage lithium-ion battery. In the embodiment of the present invention, the importance of the consistency evaluation characteristics of the three energy storage lithium-ion batteries is ranked as capacity>open circuit voltage>ohmic internal resistance.

[0164] Secondly, calculate the relative importance ratio between indicators, that is, the relative weight ratio of two adjacent indicators. The calculation formula is as follows:

[0165]

[0166] Where ηi Indicates the relative importance of the i-th indicator relative to the i+1-th indicator, ω i is the weight of the i-th indicator, ω i+1 is the weight of the i+1th indicator. In the example of the present invention, η1 is the relative importance of capacity and open circuit voltage, which is 2, and η2 is the relative importance of open circuit voltage and ohmic internal resistance, which is 1.5.

[0167] According to the above two steps, the weight coefficient of each feature can be determined by formula (19):

[0168]

[0169] Where, ω G1,j represents the weight coefficient of the jth feature obtained according to the order relationship analysis method, where k' represents the index number, which is an integer from 2 to j, and η i According to formula (18), the order of G1 weight calculation is to start from the least important feature and calculate more important features step by step.

[0170] To intuitively illustrate the calculation process of formula (19), in the present invention, there are 3 features to be evaluated, and η3 = 1. When j = 3, first calculate Here we need to calculate k"=2 and k=3" separately for discussion:

[0171] When k'=2, When k'=3, so =1.5+1=2.5, then And because η1=2, then ω G1,1 =2ω G1,2 , since the weighted sum of the three consistency evaluation features is 1, we can solve The subjective weight coefficients corresponding to capacity, open circuit voltage, and ohmic internal resistance respectively.

[0172] In step c2, ohmic internal resistance, open circuit voltage, and capacity are all used as evaluation indicators, and an indicator matrix is ​​constructed based on the evaluation indicators. Each indicator in the indicator matrix is ​​normalized, and the indicator variability and indicator conflict of each indicator after normalization are calculated respectively.

[0173] Specifically, step c1 is to calculate the subjective weight coefficients of the three evaluation characteristics of energy storage lithium-ion battery consistency based on the order relationship analysis method, and calculate the objective weight coefficients of ohmic internal resistance, open circuit voltage and capacity based on the CRITIC objective weight distribution method.

[0174] The ohmic internal resistance, open circuit voltage and capacity are all used as evaluation indicators, and an indicator matrix is ​​constructed based on the evaluation indicators. Assuming that there are b evaluation samples and there are f evaluation indicators in total, the indicator matrix of the original data can be expressed as follows:

[0175]

[0176] Among them, x ij Represents the value of the jth evaluation indicator of the i-th evaluation sample.

[0177] In order to eliminate the influence of different dimensions on the evaluation results, it is necessary to perform dimensionless processing on each indicator, that is, to perform column normalization processing on formula (20). For the first evaluation indicator, the dimensionless processing formula is as follows:

[0178]

[0179] Where x' i1 Indicates the normalized data of the first column, max(x1) indicates the maximum value of the first column, min(x1) indicates the minimum value of the first column, x i1 represents the i-th data point in the first column, where i ranges from 1, 2, …, b. The normalization formula is applicable to any column; you only need to change the column index.

[0180] Calculate indicator variability and indicator conflict. Indicator variability is expressed in the form of standard deviation. The standard deviation calculation formula for each evaluation indicator is shown as follows:

[0181]

[0182] In the formula represents the mean value of the jth indicator, S j It represents the standard deviation of the jth indicator, that is, the standard variability of the evaluation index. The larger its value is, the greater the data difference of the indicator is, the more information can be used, and more weight should be assigned to the indicator.

[0183] Indicator conflict is a measure of the correlation between the evaluation feature and other features. The lower the correlation, the more independent information the evaluation feature provides, and the higher the weight should be. It can be obtained from the following formula:

[0184]

[0185] Where R j Indicates the conflict of the jth evaluation indicator, r lj It represents the correlation between the lth evaluation index and the jth evaluation index, which can be calculated by formula (24).

[0186]

[0187] Where x' sl represents the lth evaluation index of the sth evaluation sample after normalization, x' zl It represents the jth evaluation index of the zth evaluation sample after normalization. represents the average value of the lth evaluation index after normalization, represents the average value of the j-th evaluation indicator after normalization.

[0188] Step c3, determine the information content of each indicator based on the indicator heterogeneity and indicator conflict of each indicator, and use the CRITIC objective weight allocation method to determine the objective weight coefficient of each indicator based on the information content of each indicator. The objective weight coefficient of each indicator includes the objective weight coefficient of ohmic internal resistance, the objective weight coefficient of open circuit voltage and the objective weight coefficient of capacity.

[0189] Specifically, in the embodiment of the present invention, according to the calculation formula of the conflict and variability of the evaluation indicators, the conflict and variability of each evaluation indicator can be obtained, and the information content of each indicator can be further determined. The information content of the evaluation indicator can be determined by the following formula:

[0190] G j =R j ·S j (26);

[0191] Where G j is the information content of the jth evaluation indicator. Since there are f evaluation indicators, f information amounts will be obtained. Finally, based on the CRITIC objective weight allocation method, the final weight of each evaluation indicator can be determined by formula (26).

[0192]

[0193] Where λ cc,j represents the weight coefficient of the jth evaluation indicator obtained based on the CRITIC objective weight allocation method, Indicates the sum of the information of all evaluation indicators.

[0194] Step c4, using the ensemble average method to respectively combine the subjective weight coefficient of the ohmic internal resistance and the objective weight coefficient of the ohmic internal resistance, the subjective weight coefficient of the open circuit voltage and the objective weight coefficient of the open circuit voltage, and the subjective weight coefficient of the capacity and the objective weight coefficient of the capacity to obtain the ohmic internal resistance weight coefficient, the open circuit voltage weight coefficient and the capacity weight coefficient.

[0195] Specifically, in the embodiment of the present invention, the subjective weight distribution coefficients of the consistency evaluation characteristic capacity, ohmic internal resistance and open circuit voltage of the energy storage lithium-ion battery have been obtained based on the order relationship analysis method, and the objective weight coefficients of each evaluation indicator (i.e., consistency evaluation characteristics, referred to as evaluation characteristics) have been obtained according to the CRITIC objective weight distribution method. The subjective and objective weight distribution coefficients of the evaluation characteristics are further combined based on the geometric mean method. The combined consistency evaluation feature weight coefficient can be determined by the following formula:

[0196]

[0197] Where, represents the final weight distribution coefficient after the subjective and objective weight coefficients of the jth evaluation feature are integrated, A=1,2,…,p, p represents the total number of evaluation features, λ cc,A represents the weight coefficient of the Ath evaluation feature obtained based on the CRITIC objective weight allocation method, ω G1,A Represents the weight coefficient of the Ath evaluation feature obtained based on the order relationship analysis method, The geometric mean of the subjective and objective weight coefficients of each evaluation feature is calculated, and then the sum is calculated for all evaluation features.

[0198] The energy storage battery consistency assessment method provided in this embodiment innovatively uses a subjective and objective combination method to assign weights, combines the order relationship analysis method to determine the subjective weights, and the CRITIC objective weight allocation method to determine the objective weights. The combined weights are obtained through geometric mean calculation. The subjective and objective weights are integrated through geometric mean, which not only retains the expert's understanding of the essence of the characteristics, but also dynamically adjusts according to the characteristics of the data, making the assessment more objective and comprehensive.

[0199] Step S3042, respectively calculate the first product of the ohmic internal resistance consistency evaluation score and the ohmic internal resistance weight coefficient, the second product of the open circuit voltage consistency evaluation score and the open circuit voltage weight coefficient, and the third product of the capacity consistency evaluation score and the capacity weight coefficient, and add the first product, the second product, and the third product to obtain the comprehensive consistency evaluation score of the energy storage battery to be evaluated.

[0200] Specifically, based on the combined subjective and objective weight coefficients of the capacity, ohmic internal resistance, and open-circuit voltage of the energy storage lithium-ion battery, combined with the consistency evaluation score of the single evaluation feature of the energy storage lithium-ion battery obtained in step S303, the comprehensive consistency evaluation score of the energy storage battery is determined, which can be calculated by the following formula:

[0201]

[0202] Step S305 : determining the health level of the energy storage battery to be evaluated based on the comprehensive score of the consistency evaluation of the energy storage battery to be evaluated.

[0203] Specifically, the above step S305 includes:

[0204] Step d: Determine the mapping relationship between the comprehensive consistency assessment score of the energy storage battery to be evaluated and the preset health level standard interval, and determine the health level of the energy storage battery to be evaluated based on the mapping relationship. Specifically, in an embodiment of the present invention, based on the voltage, current, and capacity of each single battery in the energy storage lithium-ion battery module, the ohmic internal resistance, open circuit voltage, and capacity of the energy storage lithium-ion battery are extracted as the consistency assessment features of the energy storage lithium-ion battery based on statistical characteristics and least squares parameter identification methods. The gray correlation analysis method is used to quantify the consistency assessment score of a single feature of the energy storage lithium-ion battery. Through a subjective and objective combination of energy storage lithium-ion battery consistency assessment feature weight distribution method, based on the mathematical relationship between the comprehensive consistency assessment score of the energy storage lithium-ion battery and each single assessment feature, the comprehensive consistency assessment score of the energy storage lithium-ion battery to be evaluated is calculated, and the health status of the energy storage lithium-ion battery to be evaluated is further determined.

[0205] Specifically, the consistency evaluation results of energy storage lithium-ion batteries are numbers between 0 and 1. The closer the evaluation results are to 1, the better the consistency of each battery. To make the results more practical, the consistency results are divided into standards and the mapping relationship between standard intervals and health levels is determined as follows:

[0206] When the consistency evaluation result is greater than 0.9, the health status of the energy storage lithium-ion battery is healthy;

[0207] When the consistency assessment result is between 0.7 and 0.9, the health status of the energy storage lithium-ion battery is sub-healthy;

[0208] When the consistency assessment result is between 0.4 and 0.7, the health state of the energy storage lithium-ion battery is degraded;

[0209] When the consistency evaluation result is less than 0.4, the health status of the energy storage lithium-ion battery is poor.

[0210] The energy storage battery consistency assessment method provided in this embodiment provides a clear and unified basis for determining the health status of energy storage batteries by presetting standard health grade intervals. Compared to vague qualitative descriptions, this quantitative mapping relationship avoids assessment bias caused by subjective judgment. It converts complex consistency assessment scores into simple health grade classifications, presenting battery health status in an intuitive manner.

[0211] As one or more specific application embodiments of the present invention, combined with Figures 4 to 6 The present invention provides a method for evaluating the consistency of energy storage batteries, which is further described in detail, specifically including:

[0212] like Figure 4 As shown, the present invention provides a method for evaluating the consistency of energy storage batteries, comprising the following steps:

[0213] Step 1: Obtain voltage data, current data, and charge and discharge curves of each battery in the series-connected battery pack to be evaluated to obtain capacity data.

[0214] Step 2: Identify the ohmic internal resistance and open circuit voltage of the energy storage lithium-ion battery based on the voltage and current data of the energy storage lithium-ion battery, and use the capacity of the energy storage lithium-ion battery as three characteristics for consistency evaluation of the energy storage lithium-ion battery.

[0215] Step 3: quantify the consistency evaluation score of a single evaluation feature of the energy storage lithium-ion battery based on the grey relational analysis method.

[0216] Step 4: Assign weight coefficients for the three characteristics of the energy storage lithium-ion battery consistency assessment based on a combination of the ordinal relationship analysis method and the CRITIC objective weight assignment method. Determine the overall consistency score for the energy storage lithium-ion battery based on the weights of the three characteristics and the consistency assessment score of the energy storage lithium-ion battery single characteristic in Step 3.

[0217] Step 5: Based on the preset mapping relationship between the comprehensive score of the energy storage lithium-ion battery consistency assessment and the overall health status of the energy storage lithium-ion battery, if the comprehensive score of the energy storage lithium-ion battery to be assessed is obtained according to steps 1 to 4, the overall health status of the energy storage lithium-ion battery is determined.

[0218] Specifically, if Figure 6 As shown:

[0219] In step 1, in an embodiment of the present invention, the energy storage battery management system is used to collect the voltage, current and capacity data of each battery cell of the energy storage lithium-ion battery to be evaluated.

[0220] In step 2, in an embodiment of the present invention, features are selected based on the principle of describing the battery's characteristics as comprehensively as possible and being easily accessible. The present invention selects the ohmic internal resistance, open-circuit voltage, and capacity of energy storage lithium-ion batteries as consistency assessment features for energy storage lithium-ion batteries. Capacity information is statistically analyzed based on data recorded by the BMS battery management system, and the capacity data is presented in the form of a time series. The ohmic internal resistance and open-circuit voltage of the energy storage lithium-ion battery are identified by establishing an equivalent first-order circuit model of the energy storage battery. Using the kinetic equation of the equivalent circuit and the least squares parameter identification method, the ohmic internal resistance and open-circuit voltage of the energy storage lithium-ion battery are used as input and the voltage of the energy storage battery as the observed quantity.

[0221] According to the electrical characteristics of energy storage lithium-ion batteries, a first-order equivalent circuit model of energy storage lithium-ion batteries is established. There are also second-order and third-order equivalent models, but the calculation process is similar and will not be repeated here. The equivalent first-order circuit of energy storage lithium-ion batteries is as follows: Figure 5 As shown, where V p Indicates polarization voltage, V ocv represents the open circuit voltage, R0 represents the ohmic internal resistance, C p Represents polarized capacitance, R p Represents polarization resistance, V t Represents the terminal voltage, I represents the current flowing through the battery, and the battery terminal voltage and current can be measured and stored by the energy storage lithium-ion battery management system.

[0222] Based on the knowledge of circuit principles, the kinetic equation of the first-order equivalent circuit of the energy storage lithium-ion battery can be established as shown below:

[0223]

[0224] Where V ocv , R0, C p and R p The parameters are all unknown, and therefore an offline or online identification method is required to identify the unknown parameters. In the embodiment of the present invention, a recursive least squares parameter identification method with a forgetting factor is adopted.

[0225] If the difference equation of the unidentified system model is:

[0226]

[0227] In the formula, y(k) represents the system output, u(k) represents the system input, e(k) represents the noise, k represents the time point, i is the index, which takes values ​​1, 2, 3…, n, and is used to traverse the number of steps of historical data, a1, a2,…, a n is the autoregressive coefficient of the system output, which is used to describe the dependence of the system output on historical values, b1, b2, ..., b n is the input response coefficient of the system, which describes the response of the system output to the past input.

[0228] If:

[0229]

[0230] Where θ is the system parameter vector to be identified, is the parameter matrix, and φ(k) is the data matrix.

[0231] The above formula can be written in the conventional form of least squares method:

[0232] y=φθ T +e(4);

[0233] Among them, θ T It represents the transposition operation of the parameter matrix, y is the result of the least squares method, and e is the system residual, that is, random white noise.

[0234] If the input and output are expanded to N dimensions, the form of the least squares method remains unchanged, and the corresponding data matrix dimension changes. The specific data matrix is ​​not given here.

[0235] Take the functional J(θ) as follows:

[0236]

[0237] The calculation principle of the least squares method is to update the parameters to be identified so that the functional takes the minimum value. Then when the functional takes the minimum value, the functional derivative is set to zero. At this time, the matrix of the parameters to be identified is expressed as:

[0238]

[0239] In order to identify the parameter matrix of the battery cell to be identified using the least squares method, the kinetic equation of the energy storage lithium-ion battery needs to be rewritten into the form of the least squares method. The discretization method is used to discretize formula (1). The differential equation of the energy storage lithium-ion battery system after processing is:

[0240]

[0241] Where Δt is the sampling period, which is 1s here.

[0242] To make the expression more intuitive and simpler, we introduce intermediate variables and rewrite the above equation as follows:

[0243] V t (k)=(1-c1)V ocv (k)+c1V t (k-1)+c2I(k)+c3I(k-1) (8);

[0244] Where V t (k) is the system output, I(k) is the system input, c1, c2, c3 are the coefficients of the discretized equations, and the coefficients of each differential equation can be determined according to (7) as follows:

[0245]

[0246] Then corresponding to the system least squares form, we can let V t (k) = y(k), that is, the terminal voltage of the battery is the output variable of the energy storage lithium-ion battery system. Considering the existence of Gaussian white noise e(k) with a mean of 0 in actual conditions, the energy storage lithium-ion battery system can be expressed as follows:

[0247] y(k)=φ(k)θ(k)+e(k) (10).

[0248] According to formula (10), the parameter matrix and data matrix in the least square form of the energy storage lithium-ion battery system can be determined as follows:

[0249]

[0250] Where θ(k) and φ(k) are the parameter matrix and data matrix, respectively. I(k) and I(k-1) are the currents flowing through the battery at the kth moment and the k-1th moment, respectively.

[0251] Establish the recursive least squares method with forgetting factor recursive equation:

[0252]

[0253] in, is the estimated value of the parameter to be identified at time k-1, is the estimated value of the parameter to be identified at time k, K(k) is the gain coefficient matrix of the algorithm at time k, y(k) is the system output at time k, φ T (k) is the transposed matrix of the system parameter matrix at time k, P(k-1) is the error covariance matrix of the predicted value of the state variable at time k-1, and λ is the forgetting factor.

[0254] Establish the system equations:

[0255]

[0256] Where Δt is the sampling period, and θ0, θ1, θ2, and θ3 are the parameter matrices identified using the recursive least squares method with a forgetting factor.

[0257] In the above step a4, the system equation is instantiated to identify the ohmic internal resistance and open circuit voltage of the energy storage battery, including:

[0258] The parameter matrix of the energy storage battery to be evaluated at all times is calculated based on the voltage and current, and the parameter matrix is ​​substituted into the system equation to identify the ohmic internal resistance and open-circuit voltage of the energy storage battery.

[0259] Furthermore, a parameter identification process based on the recursive least squares method with a forgetting factor is instantiated: the system parameters θ(0), P(0), and the forgetting factor λ are initialized. The parameter matrix θ of the battery to be evaluated at all times is calculated based on the voltage and current data collected in step S201 and formula (12). Substituting the parameter matrix into formula (13) can obtain the coefficients on the left side of formula (13), i.e., the open circuit voltage and ohmic internal resistance in the equivalent circuit to be identified. The above-mentioned initialized system parameters θ(0) are arbitrary values, P(0) = ΨI', and should be as large as possible, where I' is the unit matrix, the coefficient Ψ is 8000, and the initialized forgetting factor λ is 0.98.

[0260] In step 3, in an embodiment of the present invention, the ohmic internal resistance, open circuit voltage, and capacity of the energy storage lithium-ion battery are normalized, and the correlation between the consistency evaluation characteristics of each energy storage lithium-ion battery and the reference value is calculated using a gray correlation analysis method, and the correlation is used as the consistency evaluation score of each energy storage lithium-ion battery.

[0261] Specifically, the calculation procedure of the grey relational analysis method includes:

[0262] Determine the object to be evaluated and the index. Suppose there are h sequences to be evaluated and a reference sequence, which can be expressed as: reference sequence X0 = {x0(1), x0(2), ..., x0(o)}, comparison sequence X i ={x i (1),x i (2),...,x i (o)}, i = 1, 2, ..., h, where o represents the number of feature data collected in the current evaluation window. Since different evaluation indicators have different dimensions, the data needs to be dimensionless. The normalization calculation formula is shown as follows:

[0263]

[0264] Among them, x i (k') represents the k'th value in the reference sequence or comparison sequence, where k ranges from 1, 2, ..., h.

[0265] The normalized data matrix can be expressed as:

[0266]

[0267] The correlation coefficient is calculated to measure the closeness between the comparison sequence and the reference sequence at each evaluation point. The calculation formula is:

[0268]

[0269] in, is the absolute difference, i.e., the absolute value of the difference between the kth sample in the reference sequence and the kth sample in the comparison sequence after normalization. ρ is the resolution coefficient, which generally ranges from 0.1 to 0.5, with ρ = 0.5 often used to balance the effects of very large and very small differences.

[0270] The correlation coefficient matrix obtained by calculation of formula (13) is ξ=[ξ(1),ξ(2)…ξ(h)]. The grey correlation coefficient is obtained by averaging the correlation coefficient matrix. Its calculation formula is:

[0271]

[0272] Specifically, taking ohmic resistance as an example, the following calculation procedure is also applicable to the other two evaluation features. If a total of 5 feature points are obtained in the current evaluation time window, and there are 3 single cells in the energy storage lithium-ion battery module to be evaluated, and the data has been normalized, then the ohmic resistance feature can be expressed as:

[0273]

[0274] Each column of the above ohmic resistance characteristic matrix represents a characteristic point, and each row represents a different battery. Therefore, the data matrix has a shape of 3×5. In the embodiment of the present invention, the mean value of all batteries for the same characteristic point is used as the reference sequence. For the ohmic resistance characteristic matrix of three batteries, the reference sequence is the mean value of each column. Therefore, the shape of the ohmic resistance reference sequence is 1×5, which can be expressed as According to formula (13), the grey correlation value ξ of the three single cells to the reference sequence can be calculated: 欧姆电阻 =[ξ 欧姆电阻 (1)ξ 欧姆电阻 (2)ξ 欧姆电阻 (3)], taking the average value of the grey correlation value sequence of the ohmic resistance of the energy storage lithium-ion battery, the overall grey correlation value of the energy storage lithium-ion battery module composed of three single cells in the current time window can be obtained, which also represents the consistency evaluation result.

[0275] In step 4, based on the evaluation scores of the single characteristics of ohmic resistance, open circuit voltage and capacity of the battery pack to be tested obtained in step 3, each single evaluation characteristic is weighted to determine the mathematical relationship between the comprehensive consistency evaluation score of the energy storage lithium-ion battery to be evaluated and the consistency evaluation score of each single characteristic lithium-ion battery.

[0276] In the example of the present invention, considering that the degree of influence of each evaluation feature on the consistency of energy storage lithium-ion batteries is different, the weight of a single evaluation feature is allocated by a combined weighting method combining the ordinal relationship analysis method and the CRITIC objective weight allocation method, so as to determine the comprehensive consistency evaluation score of the energy storage lithium-ion battery based on the single feature evaluation result and the corresponding weight coefficient.

[0277] The order relationship analysis method is a subjective weighting method based on the improved hierarchical analysis method. Its basic steps include: first, it is necessary to determine the order relationship of the consistency evaluation characteristics of the energy storage lithium-ion battery. In the embodiment of the present invention, the importance of the consistency evaluation characteristics of the three energy storage lithium-ion batteries is ranked as capacity > open circuit voltage > ohmic internal resistance.

[0278] Secondly, calculate the relative importance ratio between indicators, that is, the relative weight ratio of two adjacent indicators. The calculation formula is as follows:

[0279]

[0280] Where η i Indicates the relative importance of the i-th indicator relative to the i+1-th indicator, ω i is the weight of the i-th indicator, ω i+1 In the example of the present invention, η1 is the relative importance of capacity and open circuit voltage, which is 2, and η2 is the relative importance of open circuit voltage and ohmic internal resistance, which is 1.5.

[0281] According to the above two steps, the weight coefficient of each feature can be determined by formula (19):

[0282]

[0283] Where, ω G1,j represents the weight coefficient of the jth feature obtained according to the order relationship analysis method, where k' represents the index number, which is an integer from 2 to j, and η i According to formula (18), the order of G1 weight calculation is to start from the least important feature and calculate more important features step by step.

[0284] To intuitively illustrate the calculation process of formula (19), in the present invention, there are 3 features to be evaluated, and η3 = 1. When j = 3, first calculate Here we need to calculate k"=2 and k=3" separately for discussion:

[0285] When k'=2, When k'=3, so =1.5+1=2.5, then And because η1=2, then ωG1,1 =2ω G1,2 , since the weighted sum of the three consistency evaluation features is 1, we can solve The subjective weight coefficients corresponding to capacity, open circuit voltage, and ohmic internal resistance respectively.

[0286] Step c1 is to calculate the subjective weight coefficients of the three evaluation characteristics of energy storage lithium-ion battery consistency based on the order relationship analysis method, and calculate the objective weight coefficients of ohmic internal resistance, open circuit voltage and capacity based on the CRITIC objective weight distribution method.

[0287] The ohmic internal resistance, open circuit voltage and capacity are all used as evaluation indicators, and an indicator matrix is ​​constructed based on the evaluation indicators. Assuming that there are b evaluation samples and there are f evaluation indicators in total, the indicator matrix of the original data can be expressed as follows:

[0288]

[0289] Among them, x ij Represents the value of the jth evaluation indicator of the i-th evaluation sample.

[0290] In order to eliminate the influence of different dimensions on the evaluation results, it is necessary to perform dimensionless processing on each indicator, that is, to perform column normalization processing on formula (20). For the first evaluation indicator, the dimensionless processing formula is as follows:

[0291]

[0292] Where x' i1 Indicates the normalized data of the first column, max(x1) indicates the maximum value of the first column, min(x1) indicates the minimum value of the first column, x i1 represents the i-th data point in the first column, where i ranges from 1, 2, …, b. The normalization formula is applicable to any column; you only need to change the column index.

[0293] Calculate indicator variability and indicator conflict. Indicator variability is expressed in the form of standard deviation. The standard deviation calculation formula for each evaluation indicator is shown as follows:

[0294]

[0295] In the formula represents the mean value of the jth indicator, S j It represents the standard deviation of the jth indicator, that is, the standard variability of the evaluation index. The larger its value is, the greater the data difference of the indicator is, the more information can be used, and more weight should be assigned to the indicator.

[0296] Indicator conflict is a measure of the correlation between the evaluation feature and other features. The lower the correlation, the more independent information the evaluation feature provides, and the higher the weight should be. It can be obtained from the following formula:

[0297]

[0298] Where R j Indicates the conflict of the jth evaluation indicator, r lj It represents the correlation between the lth evaluation index and the jth evaluation index, which can be calculated by formula (24).

[0299]

[0300] Where x' sl represents the lth evaluation index of the sth evaluation sample after normalization, x' zl It represents the jth evaluation index of the zth evaluation sample after normalization. represents the average value of the lth evaluation index after normalization, represents the average value of the j-th evaluation indicator after normalization.

[0301] In the embodiment of the present invention, according to the calculation formula of the conflict and variability of the evaluation indicators, the conflict and variability of each evaluation indicator can be obtained, and the information content of each indicator can be further determined. The information content of the evaluation indicator can be determined by the following formula:

[0302] G j =R j ·S j (26);

[0303] Where G j is the information content of the jth evaluation indicator. Since there are f evaluation indicators, f information amounts will be obtained. Finally, based on the CRITIC objective weight allocation method, the final weight of each evaluation indicator can be determined by formula (26).

[0304]

[0305] Where λ cc,j represents the weight coefficient of the jth evaluation indicator obtained based on the CRITIC objective weight allocation method, Indicates the sum of the information of all evaluation indicators.

[0306] In the embodiment of the present invention, the subjective weight distribution coefficients of the consistency evaluation characteristic capacity, ohmic internal resistance and open circuit voltage of the energy storage lithium-ion battery have been obtained based on the order relationship analysis method, and the objective weight coefficients of each evaluation indicator (i.e., consistency evaluation characteristics, referred to as evaluation characteristics) have been obtained according to the CRITIC objective weight distribution method. The subjective and objective weight distribution coefficients of the evaluation characteristics are further combined based on the geometric mean method. The combined consistency evaluation characteristic weight coefficient can be determined by the following formula:

[0307]

[0308] Where, represents the final weight distribution coefficient after the subjective and objective weight coefficients of the jth evaluation feature are integrated, A=1,2,…,p, p represents the total number of evaluation features, λ cc,A represents the weight coefficient of the Ath evaluation feature obtained based on the CRITIC objective weight allocation method, ω G1,A Represents the weight coefficient of the Ath evaluation feature obtained based on the order relationship analysis method, The geometric mean of the subjective and objective weight coefficients of each evaluation feature is calculated, and then the sum is calculated for all evaluation features.

[0309] Based on the subjective and objective weight coefficients of the capacity, ohmic internal resistance and open circuit voltage of the energy storage lithium-ion battery, combined with the consistency assessment score of the single assessment feature of the energy storage lithium-ion battery obtained in step 3, the comprehensive consistency assessment score of the energy storage battery is determined, which can be calculated by the following formula:

[0310]

[0311] In step 5, in an embodiment of the present invention, based on the voltage, current, and capacity of each single battery in the energy storage lithium-ion battery module, the ohmic internal resistance, open circuit voltage, and capacity of the energy storage lithium-ion battery are extracted as consistency assessment features of the energy storage lithium-ion battery based on statistical characteristics and a least squares parameter identification method. A gray correlation analysis method is used to quantify the consistency assessment score of a single feature of the energy storage lithium-ion battery. Through a weight distribution method for consistency assessment features of energy storage lithium-ion batteries that combines subjective and objective factors, and based on the mathematical relationship between the comprehensive consistency assessment score of the energy storage lithium-ion battery and each single assessment feature, a comprehensive consistency assessment score of the energy storage lithium-ion battery to be assessed is calculated, and the health status of the energy storage lithium-ion battery to be assessed is further determined.

[0312] Specifically, the consistency evaluation results of energy storage lithium-ion batteries are numbers between 0 and 1. The closer the evaluation results are to 1, the better the consistency of each battery. To make the results more practical, the consistency results are divided into standards and the mapping relationship between the standard interval and the health level is determined as follows:

[0313] When the consistency evaluation result is greater than 0.9, the health status of the energy storage lithium-ion battery is healthy;

[0314] When the consistency assessment result is between 0.7 and 0.9, the health status of the energy storage lithium-ion battery is sub-healthy;

[0315] When the consistency assessment result is between 0.4 and 0.7, the health state of the energy storage lithium-ion battery is degraded;

[0316] When the consistency evaluation result is less than 0.4, the health status of the energy storage lithium-ion battery is poor.

[0317] The energy storage battery consistency evaluation method provided in this embodiment constructs multiple consistency evaluation indicators to more comprehensively reflect the status of the energy storage lithium-ion battery, avoid the limitations of the single feature evaluation method, and improve the accuracy of the consistency evaluation results. Based on the weight allocation method of subjective and objective combination, the weights of capacity, ohmic internal resistance and open circuit voltage are scientifically and objectively assigned. In summary, the present invention can provide comprehensive and objective lithium-ion battery consistency evaluation results, help operation and maintenance personnel optimize battery management, and thus improve the safety, stability and service life of the energy storage system.

[0318] This embodiment also provides a device for evaluating the consistency of an energy storage battery. This device is used to implement the above-mentioned embodiments and preferred embodiments. Details that have already been described will not be repeated. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0319] This embodiment provides a device for evaluating the consistency of an energy storage battery. Figure 7 Shown, including:

[0320] The data acquisition module 701 is used to obtain the voltage, current and capacity of the energy storage battery to be evaluated.

[0321] The feature extraction module 702 is used to extract consistency evaluation features of the energy storage battery to be evaluated based on voltage, current and capacity.

[0322] The consistency quantification module 703 is used to quantify the consistency evaluation score of each single feature in the consistency evaluation features.

[0323] The comprehensive score determination module 704 is configured to assign a weight to each single feature in the consistency evaluation feature, and determine a comprehensive consistency evaluation score of the energy storage battery to be evaluated based on the consistency evaluation score of each single feature and the weight of each single feature.

[0324] The evaluation module 705 is configured to determine the health level of the energy storage battery to be evaluated based on the comprehensive score of the consistency evaluation of the energy storage battery to be evaluated.

[0325] In some optional implementations, the feature extraction module 702 includes:

[0326] The feature extraction unit is used to extract the ohmic internal resistance and open circuit voltage of the energy storage battery based on the voltage and current, and use the ohmic internal resistance, open circuit voltage and capacity as consistency evaluation features of the energy storage battery to be evaluated.

[0327] In some optional implementations, the feature extraction unit includes:

[0328] The battery kinetic equation establishment subunit is used to establish the energy storage battery kinetic equation based on voltage and current.

[0329] The recursive least squares method equation establishment subunit with forgetting factor is used to discretize the energy storage battery kinetic equation and establish the recursive least squares method equation with forgetting factor based on the discretized equation.

[0330] The recursive least squares method with forgetting factor recursive equation establishing subunit is used to establish the parameter matrix equation and the data matrix based on the recursive least squares method with forgetting factor, and to establish the recursive least squares method with forgetting factor recursive equation based on the parameter matrix and the data matrix.

[0331] The ohmic internal resistance and open-circuit voltage identification subunit is used to establish the system equation of the energy storage battery based on the recursive least squares method with a forgetting factor, and instantiate the system equation to identify the ohmic internal resistance and open-circuit voltage of the energy storage battery. Instantiating the system equation to identify the ohmic internal resistance and open-circuit voltage of the energy storage battery includes calculating a parameter matrix of the energy storage battery to be evaluated at all times based on voltage and current, and substituting the parameter matrix into the system equation to identify the ohmic internal resistance and open-circuit voltage of the energy storage battery.

[0332] In some optional implementations, the consistency quantification module 703 includes:

[0333] The grey relational analysis method quantification unit is used to quantify the consistency evaluation score of each single feature in the consistency evaluation feature using the grey relational analysis method, wherein the formula of the grey relational analysis method is expressed as:

[0334]

[0335] Among them, ξ i (k') is the consistency evaluation score for each single feature, is the absolute value difference, represents the reference sequence, represents the sample sequence to be evaluated, and ρ is the resolution coefficient.

[0336] In some optional implementations, the comprehensive score determination module 704 includes:

[0337] The subjective and objective weight coefficient calculation unit is used to assign weights to the ohmic internal resistance, open circuit voltage and capacity respectively by using the order relationship analysis method and the CRITIC objective weight allocation method, and obtain the ohmic internal resistance weight coefficient, the open circuit voltage weight coefficient and the capacity weight coefficient.

[0338] The consistency assessment comprehensive score calculation unit is used to respectively calculate the first product of the ohmic internal resistance consistency assessment score and the ohmic internal resistance weight coefficient, the second product of the open circuit voltage consistency assessment score and the open circuit voltage weight coefficient, and the third product of the capacity consistency assessment score and the capacity weight coefficient, and add the first product, the second product and the third product to obtain the consistency assessment comprehensive score of the energy storage battery to be evaluated.

[0339] In some optional implementations, the subjective and objective weight coefficient calculation unit includes:

[0340] The subjective weight coefficient calculation subunit is used to rank the importance of ohmic internal resistance, open circuit voltage and capacity using the order relationship analysis method, and calculate the relative weight ratio between ohmic internal resistance, open circuit voltage and capacity based on the order of importance ranking, and obtain the subjective weight coefficient of ohmic internal resistance, open circuit voltage and capacity based on the relative weight ratio between them.

[0341] The objective weight coefficient calculation subunit is used to take ohmic internal resistance, open circuit voltage and capacity as evaluation indicators, construct an indicator matrix based on the evaluation indicators, normalize each indicator in the indicator matrix, and calculate the indicator variability and indicator conflict of each indicator after normalization; determine the information content of each indicator based on the indicator variability and indicator conflict of each indicator, and use the CRITIC objective weight allocation method to determine the objective weight coefficient of each indicator based on the information content of each indicator. The objective weight coefficient of each indicator includes the objective weight coefficient of ohmic internal resistance, the objective weight coefficient of open circuit voltage and the objective weight coefficient of capacity.

[0342] The combined weight coefficient calculation subunit is used to combine the subjective weight coefficient of the ohmic internal resistance with the objective weight coefficient of the ohmic internal resistance, the subjective weight coefficient of the open circuit voltage with the objective weight coefficient of the open circuit voltage, and the subjective weight coefficient of the capacity with the objective weight coefficient of the capacity by using the ensemble average method to obtain the ohmic internal resistance weight coefficient, the open circuit voltage weight coefficient and the capacity weight coefficient.

[0343] In some optional implementations, the evaluation module 705 includes:

[0344] The mapping unit is used to determine a mapping relationship between the comprehensive consistency assessment score of the energy storage battery to be evaluated and a preset health level standard range, and determine the health level of the energy storage battery to be evaluated based on the mapping relationship.

[0345] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0346] The energy storage battery consistency evaluation device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0347] The embodiment of the present invention also provides a computer device having the above Figure 7 The energy storage battery consistency evaluation device shown.

[0348] See also Figure 8 , Figure 8 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 8 As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 8 A processor 10 is taken as an example.

[0349] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0350] The memory 20 stores instructions that can be executed by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0351] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0352] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0353] The computer device further includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 8 The bus connection is taken as an example.

[0354] The input device 30 can receive input digital or character information and generate key signal input related to user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a trackpad, a touch pad, an indicator stick, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 can include a display device, an auxiliary lighting device (e.g., an LED), and a tactile feedback device (e.g., a vibration motor). The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display, and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0355] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0356] A portion of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the form in which the computer program instruction exists in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc. Accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by the computer.

[0357] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for evaluating the consistency of an energy storage battery, characterized in that: The method comprises: Obtain the voltage, current and capacity of the energy storage battery to be evaluated; Extracting consistency assessment features of the energy storage battery to be evaluated based on the voltage, current, and capacity; quantifying the consistency assessment score of each single feature in the consistency assessment features; Assigning a weight to each single feature in the consistency evaluation feature, and determining a comprehensive consistency evaluation score of the energy storage battery to be evaluated based on the consistency evaluation score of each single feature and the weight of each single feature; The health level of the energy storage battery to be evaluated is determined based on the comprehensive score of the consistency evaluation of the energy storage battery to be evaluated.

2. The method according to claim 1, characterized in that Based on the voltage, current, and capacity, the consistency assessment features of the energy storage battery to be evaluated are extracted, including: The ohmic internal resistance and open circuit voltage of the energy storage battery are extracted based on the voltage and current, and the ohmic internal resistance, open circuit voltage and capacity are used as consistency evaluation features of the energy storage battery to be evaluated.

3. The method according to claim 2, characterized in that The step of extracting the ohmic internal resistance and open circuit voltage of the energy storage battery based on the voltage and current includes: Establishing a kinetic equation for an energy storage battery based on the voltage and current; Discretizing the energy storage battery kinetic equation, and establishing a recursive least squares equation with a forgetting factor based on the discretized equation; Determining a parameter matrix and a data matrix based on the recursive least squares method equation with a forgetting factor, and establishing a recursive least squares method recursive equation with a forgetting factor based on the parameter matrix and the data matrix; A system equation of the energy storage battery is established based on the recursive least squares method with a forgetting factor, and the system equation is instantiated to identify the ohmic internal resistance and open circuit voltage of the energy storage battery.

4. The method according to claim 3, characterized in that Instantiate the system equations to identify the ohmic internal resistance and open circuit voltage of the energy storage battery, including: The parameter matrix of the energy storage battery to be evaluated at all times is calculated based on the voltage and current, and the parameter matrix is ​​substituted into the system equation to identify the ohmic internal resistance and open circuit voltage of the energy storage battery.

5. The method according to claim 1, characterized in that The quantifying the consistency assessment score of each single feature in the consistency assessment features includes: The grey relational analysis method is used to quantify the consistency evaluation score of each single feature in the consistency evaluation feature, wherein the grey relational analysis method is expressed as follows: Among them, ξ i (k') is the consistency evaluation score for each single feature, is the absolute value difference, represents the reference sequence, represents the sample sequence to be evaluated, and ρ is the resolution coefficient.

6. The method according to claim 3, characterized in that Assign a weight to each single feature in the consistency assessment feature, and determine the comprehensive consistency assessment score of the energy storage battery to be assessed based on the consistency assessment score of each single feature and the weight of each single feature, including: The order relationship analysis method and CRITIC objective weight allocation method are used to assign weights to ohmic internal resistance, open circuit voltage and capacity respectively, and the ohmic internal resistance weight coefficient, open circuit voltage weight coefficient and capacity weight coefficient are obtained; The first product of the ohmic internal resistance consistency assessment score and the ohmic internal resistance weight coefficient, the second product of the open circuit voltage consistency assessment score and the open circuit voltage weight coefficient, and the third product of the capacity consistency assessment score and the capacity weight coefficient are calculated respectively, and the first product, the second product and the third product are added together to obtain the comprehensive consistency assessment score of the energy storage battery to be evaluated.

7. The method according to claim 6, characterized in that The method of using the order relationship analysis method and the CRITIC objective weight allocation method to allocate weights to the ohmic internal resistance, open circuit voltage and capacity respectively, and obtain the ohmic internal resistance weight coefficient, open circuit voltage weight coefficient and capacity weight coefficient, including: The order relationship analysis method is used to rank the importance of ohmic internal resistance, open circuit voltage and capacity, and the relative weight ratios between ohmic internal resistance, open circuit voltage and capacity are calculated based on the order of importance. Based on the relative weight ratios between the two, the subjective weight coefficients of ohmic internal resistance, open circuit voltage and capacity are obtained; The ohmic internal resistance, open circuit voltage, and capacity are all used as evaluation indicators, and an indicator matrix is ​​constructed based on the evaluation indicators. Each indicator in the indicator matrix is ​​normalized, and the indicator variability and indicator conflict of each indicator after the normalization are calculated respectively; Determine the information content of each indicator based on the indicator heterogeneity and indicator conflict of each indicator, and use the CRITIC objective weight allocation method to determine the objective weight coefficient of each indicator based on the information content of each indicator, wherein the objective weight coefficient of each indicator includes the objective weight coefficient of ohmic internal resistance, the objective weight coefficient of open circuit voltage, and the objective weight coefficient of capacity; The subjective weight coefficient of the ohmic internal resistance and the objective weight coefficient of the ohmic internal resistance, the subjective weight coefficient of the open-circuit voltage and the objective weight coefficient of the open-circuit voltage, and the subjective weight coefficient of the capacity and the objective weight coefficient of the capacity are respectively combined using the ensemble average method to obtain the ohmic internal resistance weight coefficient, the open-circuit voltage weight coefficient and the capacity weight coefficient.

8. The method according to claim 1, characterized in that Based on the comprehensive score of the consistency assessment of the energy storage battery to be assessed, the health level of the energy storage battery to be assessed is determined, including: Determine a mapping relationship between the consistency assessment comprehensive score of the energy storage battery to be assessed and a preset health grade standard interval, and determine the health grade of the energy storage battery to be assessed based on the mapping relationship.

9. A device for evaluating the consistency of an energy storage battery, characterized in that: The device comprises: A data acquisition module is used to obtain the voltage, current and capacity of the energy storage battery to be evaluated; A feature extraction module, configured to extract consistency assessment features of the energy storage battery to be assessed based on the voltage, current, and capacity; A consistency quantification module, configured to quantify the consistency evaluation score of each single feature in the consistency evaluation features; A comprehensive score determination module, configured to assign a weight to each single feature in the consistency evaluation features, and determine a comprehensive consistency evaluation score of the energy storage battery to be evaluated based on the consistency evaluation score of each single feature and the weight of each single feature; An evaluation module is used to determine the health level of the energy storage battery to be evaluated based on the comprehensive score of the consistency evaluation of the energy storage battery to be evaluated.

10. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the energy storage battery consistency assessment method according to any one of claims 1 to 8 by executing the computer instructions.

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